Rasmus Kyng: Catalogue data in Spring Semester 2021
|Name||Prof. Dr. Rasmus Kyng|
|Field||Theoretical Computer Science|
Professur Theoretische Informatik
ETH Zürich, CAB H 33.1
|Relationship||Assistant Professor (Tenure Track)|
|252-4225-00L||Presenting Theoretical Computer Science |
Number of participants limited to 24.
The deadline for deregistering expires at the end of the second week of the semester. Students who are still registered after that date, but do not attend the seminar, will officially fail the seminar.
|2 credits||2S||B. Gärtner, M. Ghaffari, R. Kyng, D. Steurer, E. Welzl|
|Abstract||Students present current or classical results from theoretical computer science.|
|Objective||Students learn to read, understand and present results from theoretical computer science. The main focus and deliverable is a good presentation of 45 minutes that can easily be followed and understood by the audience.|
|Content||Students present current or classical results from theoretical computer science.|
|Prerequisites / Notice||The seminar takes place as a block seminar on two Saturdays in April and/or May. Each presentation is jointly prepared and given by two students (procedure according to the seminar's Moodle page).|
All students must attend all presentations. Participation requires successful completion of the first year, or instructor approval.
|263-4400-00L||Advanced Graph Algorithms and Optimization||8 credits||3V + 1U + 3A||R. Kyng, M. Probst|
|Abstract||This course will cover a number of advanced topics in optimization and graph algorithms.|
|Objective||The course will take students on a deep dive into modern approaches to|
graph algorithms using convex optimization techniques.
By studying convex optimization through the lens of graph algorithms,
students should develop a deeper understanding of fundamental
phenomena in optimization.
The course will cover some traditional discrete approaches to various graph
problems, especially flow problems, and then contrast these approaches
with modern, asymptotically faster methods based on combining convex
optimization with spectral and combinatorial graph theory.
|Content||Students should leave the course understanding key|
concepts in optimization such as first and second-order optimization,
convex duality, multiplicative weights and dual-based methods,
acceleration, preconditioning, and non-Euclidean optimization.
Students will also be familiarized with central techniques in the
development of graph algorithms in the past 15 years, including graph
decomposition techniques, sparsification, oblivious routing, and
spectral and combinatorial preconditioning.
|Prerequisites / Notice||This course is targeted toward masters and doctoral students with an|
interest in theoretical computer science.
Students should be comfortable with design and analysis of algorithms, probability, and linear algebra.
Having passed the course Algorithms, Probability, and Computing (APC) is highly recommended, but not formally required. If you are not
sure whether you're ready for this class or not, please consult the